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[3/8] Add the external-draft recipe and training entrypoint - #2637
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Check name Status Explanation Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request. Security Anti-Patterns No stated security anti-pattern was introduced. The Python diff adds no torch.load(weights_only=False), numpy.load(..., allow_pickle=True), eval/exec, or # nosec usage. New Transformers loads pass rec… Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly identifies the main changes: adding the external-draft recipe and its training entrypoint.
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Review comments at @examples/speculative_decoding/main.py:
- Around line 278-322: Ensure HF-format checkpoint restores for
ModelOptExternalDraftRecipe reattach the base head before training: extract the
base-loading and attachment logic into a helper, call it after restore, load the
base with load_vlm_or_llm in offline-training mode, find its final norm via
_FINAL_NORM_PATHS, and pass it with the output embeddings to
attach_base_lm_head. Confirm the restored model retains external mode before
calling that method.
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CHANGELOG.rstexamples/speculative_decoding/README.mdexamples/speculative_decoding/collect_hidden_states/common.pyexamples/speculative_decoding/collect_hidden_states/compute_hidden_states_hf.pyexamples/speculative_decoding/eagle_utils.pyexamples/speculative_decoding/main.pymodelopt/recipe/config.pymodelopt/recipe/loader.pymodelopt/torch/speculative/__init__.pymodelopt/torch/speculative/config.pymodelopt/torch/speculative/eagle/utils.pymodelopt/torch/speculative/external/__init__.pymodelopt/torch/speculative/external/conversion.pymodelopt/torch/speculative/external/external_model.pymodelopt/torch/speculative/mode.pymodelopt/torch/speculative/plugins/__init__.pymodelopt/torch/speculative/plugins/hf_external.pymodelopt_recipes/general/speculative_decoding/external_draft.yamltests/gpu/torch/speculative/plugins/test_hf_external.pytests/unit/recipe/test_loader.pytests/unit/torch/speculative/plugins/test_hf_speculative_offline.pytests/unit/torch/speculative/test_external_draft.pytests/unit/torch/speculative/test_external_draft_offline.py
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Review comments at @modelopt/recipe/config.py:
- Line 562: Update the ModelOptExternalDraftRecipe validation in
_derive_external_offline to reject data.mode == "online" with a clear ValueError
before deriving external_offline; continue allowing offline and streaming modes.
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| trust_remote_code=recipe.model.trust_remote_code, | ||
| config_overrides=recipe.model.config_overrides, | ||
| ) | ||
| model, tokenizer = setup_external_draft( |
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[CRITICAL ModeState] The HF-resume path double-converts the draft, which breaks the exported architectures name — the exact failure save_pretrained was overridden to prevent.
What happens. mto.enable_huggingface_checkpointing() (line 72) patches PreTrainedModel.save_pretrained → _new_save_pretrained, which writes modelopt_state.pth into the save directory whenever ModeloptStateManager.is_converted(self). The external draft is converted, and HF Trainer._save goes through model.save_pretrained, so every checkpoint this mode writes carries modelopt_state.pth alongside model.safetensors.
On resume, load_vlm_or_llm(checkpoint, ...) at line 293 hits the patched from_pretrained, whose _patch_model_init_for_modelopt calls restore_from_modelopt_state inside __init__. That applies the external mode and hands back a model that is already an ExternalDraftLlamaForCausalLM. setup_external_draft then calls mtsp.convert(draft, [("external", cfg)]) a second time.
So the premise in the comment above — "The checkpoint holds the draft as a plain HF model, so it still needs converting" — does not hold for checkpoints this mode produces.
Why it matters. Two concrete consequences:
DynamicModule.convertcallsappend_level(original_cls)withoriginal_cls = type(module), which on the second pass is the first dynamic class.self.original_cls.__name__insave_pretrainedtherefore returns"ExternalDraftLlamaForCausalLM"instead of"LlamaForCausalLM", and the resumed run exportsconfig.jsonwitharchitectures: ["ExternalDraftLlamaForCausalLM"]— unloadable by vLLM/TRT-LLM/SGLang, which is precisely what that override exists to fix. A resumed run silently produces a broken checkpoint.apply_modeseesis_converted(model)→init_state=False→manager.add_mode("external", ...)appends, somodelopt_stateaccumulates one extraexternalentry per resume. Restoring that state then applies the mode N times, compounding (1).
ExternalDraftDMRegistry.register_subclass does not short-circuit this: __contains__ → _get_registered_nn_class requires nn_cls.forward is nn_cls_.forward, and the dynamic class's forward is HFExternalDraftModel.forward, so the already-converted class is never recognized as registered.
Suggested fix. Skip conversion when the restore hook already applied the mode, and only attach the head. Something like:
from modelopt.torch.opt.conversion import ModeloptStateManager
...
if not ModeloptStateManager.is_converted(draft):
mtsp.convert(draft, [("external", external_cfg)])
draft.validate_against_base(base_vocab_size)
draft.validate_tokenizer_against_base(tokenizer, base_tokenizer)(The _base_lm_head / _base_final_norm attachment must still run unconditionally, as the docstring correctly notes — those are set via object.__setattr__ and are not part of modelopt_state.)
Worth a test that round-trips save_pretrained → from_pretrained → setup_external_draft and asserts config.json's architectures is still the draft's own class and that modelopt_state holds exactly one external entry. That test fails against the current code.
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Confirmed, and it is worse than a bad architectures name: the resume path crashes. Repro on 5dd4df4 (CPU, tiny Llama, mto.enable_huggingface_checkpointing() as in main.py):
- convert to
external, thensave_pretrained. The directory containsmodelopt_state.pth. AutoModelForCausalLM.from_pretrained(dir)returnsExternalDraftLlamaForCausalLM, andis_convertedisTrue.mtsp.convert(r, [("external", {})]), whichsetup_external_draftdoes, raises:TypeError: Cannot create a consistent method resolution order (MRO) for bases ExternalDraftPreTrainedModel, ExternalDraftLlamaForCausalLM
So any HF-format resume of this mode fails at startup. The skip-convert-when-already-converted fix suggested above (while still attaching the head and running the validations) plus a save, from_pretrained, setup_external_draft round-trip test would cover it. The docstring and comment saying the checkpoint is "a plain HF model" need updating too.
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You're right, and thanks for actually running it — the docstring claiming the
checkpoint is "a plain HF model" was just wrong. enable_huggingface_checkpointing()
puts modelopt_state.pth in every checkpoint this mode writes, so from_pretrained
hands back an already-converted draft and the second convert dies on the MRO.
Now guarded with ModeloptStateManager.is_converted; the validations and the
lm_head attach still run unconditionally, since the base head isn't part of the
draft's state and a resume genuinely does need re-attaching. Docstring and the
resume-path comment corrected.
Added the round-trip you suggested: convert -> save_pretrained ->
from_pretrained -> setup_external_draft, plus one asserting the exported
architectures stays ["LlamaForCausalLM"]. Both fail on the old code.
| # outputs at all. "streaming" feeds the same pre-computed hidden states offline | ||
| # does, so it is permitted but untested for this mode -- if its batches ever lack | ||
| # base_model_outputs the forward raises rather than training on nothing. | ||
| if recipe.data.mode == "online": |
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[IMPORTANT Compatibility] This gate makes --dry_run unusable for the new mode.
The shipped external_draft.yaml carries no data.offline_data_path, so DataArguments._check_mode_requirements derives data.mode == "online" (exactly what the new test_recipe_validates_without_a_data_path_so_overrides_stay_reachable asserts). setup_external_draft runs before the if dry_run: block at line 388, so python main.py --config external_draft.yaml --dry_run model.model_name_or_path=... draft_model_name_or_path=... always dies here.
That contradicts the precedent set 60 lines below in the same function: line 259 deliberately writes if not dry_run and recipe.data.mode in ("online", "streaming") and not recipe.data.data_path: — dry-run is explicitly exempted from data-source requirements, because its whole purpose is to validate the recipe and emit an untrained HF checkpoint without touching a dataset. Every other speculative mode can be dry-run from its shipped recipe; this one cannot.
Fix: thread dry_run in and skip the mode check, matching line 259:
def setup_external_draft(recipe, training_args, base_model, draft=None, dry_run=False):
...
if not dry_run and recipe.data.mode == "online":
raise ValueError(...)and pass dry_run=dry_run at both call sites (lines 324 and 376).
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Agree. setup_external_draft runs (L376) before the if dry_run: block (L388), and the shipped recipe has no data path, so --dry_run always raises here. Gate it with not dry_run like L259.
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Agreed. Gated with not dry_run to match L259, and threaded dry_run through
both call sites. Test covers it: the shipped recipe derives mode="online", so
setup_external_draft raises without dry_run and succeeds with it.
| # every rank. training_args.device is the device accelerate assigned this process, | ||
| # which is correct both when every rank sees all GPUs and when CUDA_VISIBLE_DEVICES | ||
| # pins one GPU per rank -- local_rank arithmetic gets the second case wrong. | ||
| draft.attach_base_lm_head( |
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[CRITICAL Algorithm] base_model.get_output_embeddings() is not verified against FakeBaseModel, which is the shipped recipe's default base — and a None here trains the draft against nothing detectable.
external_draft.yaml sets use_fake_base_for_offline: true, and both call sites pass use_offline_training=True, so load_vlm_or_llm returns FakeBaseModel.from_source(...) on the default path. FakeBaseModel subclasses PreTrainedModel and holds its head at self.lm_head (modeling_fakebase.py:179), but defines no get_output_embeddings / _tied_weights_keys override — the only method it declares is forward (line 342). transformers' base implementation returns the head only when the subclass opts in; otherwise it returns None.
If it does return None, attach_base_lm_head(None, ...) stores head = copy.deepcopy(None) = None under _base_lm_head. The getattr(self, "_base_lm_head", None) is None guard in _teacher_logits then fires on the first training step — so the default-recipe path fails immediately rather than training silently. That's the better of the two outcomes, but it means the mode's documented default invocation does not run at all.
This is exactly the class of bug the PR body says end-to-end validation caught for the final norm, and it's the same root cause one line up: find_final_norm_path was introduced precisely because the base's parts are not where a real causal LM keeps them on the fake base. The head needs the same treatment, and notably HFEagleModel/HFDFlashModel already resolve theirs through _LM_HEAD_PATHS (hf_eagle.py:121, hf_dflash.py:239) rather than get_output_embeddings().
The new test_external_draft.py does not cover this: test_attaching_the_base_head_does_not_touch_the_callers_modules calls base.get_output_embeddings() on _tiny_causal_lm() — a real HF causal LM — and grep FakeBase tests/unit/torch/speculative/test_external_draft.py returns nothing. So the fake-base path, the default, is untested.
Suggested fix — resolve through the shared path list, mirroring the norm lookup directly above:
from modelopt.torch.speculative.plugins.modeling_fakebase import (
_LM_HEAD_PATHS,
find_final_norm_path,
)
base_lm_head = base_model.get_output_embeddings()
if base_lm_head is None:
for path in _LM_HEAD_PATHS:
try:
base_lm_head = base_model.get_submodule(path)
break
except AttributeError:
continue
if base_lm_head is None:
raise ValueError(
f"could not resolve an lm_head on the base model ({type(base_model).__name__}); "
"the external draft is scored against its projection of the dumped hidden states."
)Given find_final_norm_path was just added for the identical problem, generalising it to take the candidate list (or adding a sibling find_lm_head_path) would keep this to one mechanism rather than two. Please confirm with a test that builds a FakeBaseModel and runs setup_external_draft end to end — that is the configuration the README's command line actually selects.
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I checked this one and it does not hold. On transformers 5.9, PreTrainedModel.get_output_embeddings returns self.lm_head whenever the model has an lm_head and get_input_embeddings() does not raise, which is true for FakeBaseModel. FakeBaseModel(FakeBaseConfig(...)).get_output_embeddings() is model.lm_head evaluates to True. So the default fake-base path does get the head. A fake-base test of setup_external_draft would still be worthwhile, but no fix is needed here.
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Yeah, Keval's right, nothing to fix here. FakeBaseModel picks up its head fine through the base implementation.
Fake-base coverage is still worth having though. The new entrypoint tests already run setup_external_draft end to end, so I'd add a fake-base variant there rather than a standalone test.
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Re-review of 5dd4df4. Locally, tests/unit/recipe, test_external_draft.py and test_external_draft_offline.py give 532 passed. I also checked the claude[bot] findings on this commit against the code and replied in each thread.
Addressed from my last review
- The PR now targets
mainand contains only this slice. - The
sparse_data_pathleak is gone, and the recipe test moved totests/unit/recipe/. - The resume path loads the base with the same arguments as the fresh path.
find_final_norm_pathis shared by Eagle, DFlash and the example.attach_base_lm_headnow receivesdevice=training_args.device.- The CHANGELOG entry moved to 0.49.
Blocker
- HF-format resume crashes (bot thread on L324, confirmed with a CPU repro). Checkpoints this mode writes include
modelopt_state.pth, sofrom_pretrainedreturns an already-convertedExternalDraftLlamaForCausalLM.setup_external_draftthen converts it again, which raisesTypeError: Cannot create a consistent method resolution order. Skipmtsp.convertwhenModeloptStateManager.is_converted(draft), still run the validations andattach_base_lm_head, and add a save,from_pretrained,setup_external_draftround-trip test.
Should fix
2. --dry_run with the shipped recipe always raises in the online-mode gate (bot thread on L198, confirmed). Gate it on not dry_run, as L259 does.
3. find_final_norm_path can miss a norm instance registered at two paths (bot thread on modeling_fakebase L71, confirmed with a synthetic module). Use named_modules(remove_duplicate=False), or keep a get_submodule loop.
Not an issue
- The bot's "
FakeBaseModelhas no output embeddings" finding (L239) is incorrect: on transformers 5.9get_output_embeddings()returnsFakeBaseModel.lm_head. Details are in the thread.
Still open from last time (no reply yet)
cp_size > 1is still accepted for this recipe, andmodel.eagle_cp_sizeis set on the external draft. Either reject it insetup_external_draftor document it as unsupported.- The base tokenizer is still loaded twice on the fresh path (minor).
|
/claude review focus on examples/speculative_decoding/main.py and |
| external_cfg: dict = recipe.external.model_dump() | ||
| # The draft's own tokenizer; the base's is used only for its lm_head. | ||
| tokenizer = transformers.AutoTokenizer.from_pretrained( | ||
| recipe.draft_model_name_or_path, | ||
| model_max_length=training_args.training_seq_len, | ||
| trust_remote_code=recipe.model.trust_remote_code, | ||
| ) | ||
| if not ModeloptStateManager.is_converted(draft): | ||
| mtsp.convert(draft, [("external", external_cfg)]) |
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[IMPORTANT Compatibility] The new is_converted guard correctly fixes the MRO crash, but it also makes the recipe's whole external: section silently inert on the resume path.
restore_from_modelopt_state replays the config saved in the checkpoint (conversion.py:255 rebuilds config_class(**config) from modelopt_state_dict), not the one in the recipe. So when is_converted(draft) is true, external_cfg computed on line 221 is dead, and external_loss, external_top_k, external_top_p and external_report_acc all keep their checkpoint values.
Why it matters: resuming a run with an edited recipe is exactly when a user changes these. Someone who trains an epoch with external_loss: tvd, then resumes with tvd_deploy to tune for the serving policy, gets a run that reports tvd_deploy in the pprint(recipe) banner at line 280 and in the saved recipe, while actually optimising plain tvd. That is the same silent-corruption class the PR description says end-to-end validation surfaced four of — it produces a plausible loss curve and the wrong draft, with nothing in the logs to contradict the recipe.
Either re-apply the config on top of the restored draft, or refuse the mismatch. Re-applying is the smaller diff and matches what the user asked for — modify() is idempotent and is what restore_external_draft_model itself calls:
external_cfg: dict = recipe.external.model_dump()
...
if ModeloptStateManager.is_converted(draft):
# from_pretrained restored the mode from modelopt_state.pth, replaying the
# config saved with the checkpoint. Re-apply the recipe's so an edited
# external: section takes effect on resume instead of being discarded.
draft.modify(ExternalDraftConfig(**external_cfg))
else:
mtsp.convert(draft, [("external", external_cfg)])If instead the intent is that these are frozen for the life of a run, raise when the restored values differ from the recipe's rather than ignoring them — silently honouring neither is the one outcome worth avoiding.
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Claude review — focused re-review of the three prior findings
Scope applied. Per the trigger comment, I reviewed only examples/speculative_decoding/main.py and modelopt/torch/speculative/plugins/modeling_fakebase.py, and skipped tests and docs. I read the recipe/plugin context those two files depend on (hf_external.py, external/conversion.py, mode.py, opt/conversion.py, opt/plugins/huggingface.py, load_vlm_or_llm, the shipped external_draft.yaml) to check the fixes end to end, but did not review the tests/, README.md or CHANGELOG.rst changes.
Findings: CRITICAL 0 · IMPORTANT 2 · SUGGESTION 0
The three prior findings are all resolved
1. Double-convert on the HF resume path — fixed. main.py:228 now guards mtsp.convert with ModeloptStateManager.is_converted(draft), and the validations plus attach_base_lm_head still run unconditionally below it, which is what the resume path needs. I traced the mechanism: _patch_model_init_for_modelopt calls restore_from_modelopt_state inside the patched __init__ (opt/plugins/huggingface.py:90-93), and _new_save_pretrained writes modelopt_state.pth whenever the model is converted (:110-112), so a checkpoint from this mode does come back already converted and the second convert would indeed have hit the MRO error from register_subclass. The guard is the right shape.
2. The dry_run gate — fixed. main.py:207 is now if not dry_run and recipe.data.mode == "online". This matters for the shipped recipe specifically: external_draft.yaml ships every data-source field unset, and DataArguments recomputes mode from those fields (hf_training_args.py:96), so the shipped recipe always resolves to "online" — before the fix --dry_run could not complete even once against the file as shipped. It now matches the shape of the data.data_path check at :269, and the docstring says so.
3. named_modules aliasing — fixed. find_final_norm_path uses a get_submodule loop over _FINAL_NORM_PATHS, catching AttributeError per path. get_submodule walks the attribute path and is indifferent to aliasing, so a norm instance also registered under an earlier path is still found — the named_modules() de-duplication hole is closed. The docstring records the why, which is the right call since the obvious refactor back to a dict would silently reintroduce it. The helper is now the single implementation: hf_eagle.py:140 and hf_dflash.py:252 both call it, and the only remaining direct _FINAL_NORM_PATHS use is the unrelated weight-key lookup at modeling_fakebase.py:346. Replacing the old except Exception with except AttributeError also narrows what gets swallowed, which is an improvement over what it replaced.
Two new issues, both in the new guard
Both sit on the same two lines the double-convert fix introduced, and both are the kind of thing a narrow fix tends to leave behind.
The recipe external: section is silently inert on resume (main.py:221-229). Skipping convert also skips modify, and restore_from_modelopt_state replays the config saved in the checkpoint (conversion.py:255), not the recipe's. So external_cfg is computed and discarded, and resuming with an edited external_loss / external_top_k / external_top_p trains against the old objective while the recipe banner and the saved recipe both report the new one. Same silent-corruption class as the four defects the description says end-to-end validation surfaced. Re-apply modify() on the restored draft, or raise on a mismatch — the one outcome to avoid is honouring neither.
is_converted is mode-agnostic (main.py:228). It tests for any modelopt state (conversion.py:117 walks named_modules for the state key, with no registry reference), so an already-quantized draft skips conversion, is never an ExternalDraftModel, and dies on the next line with a bare AttributeError: no attribute validate_against_base instead of a clear refusal. The docstring states the narrower intent; has_state_for_mode_type("speculative", ...) expresses it.
What else I checked and found sound
The resume-path load_vlm_or_llm call now passes use_offline_training=True alongside use_fake_base, so it reaches the FakeBaseModel branch at utils.py:668 — the earlier CRITICAL about materializing every base decoder layer on resume is resolved, and the comment at :322-325 now describes what the code does. device=training_args.device on attach_base_lm_head is the right choice over local_rank arithmetic for the CUDA_VISIBLE_DEVICES-pinned case, and attach_base_lm_head deep-copies before freezing, so requires_grad=False never reaches the base's own (possibly tied) head. aux_hidden_states_optional has a real runtime path through eagle_utils.py:176 to OfflineSupervisedDataset and stays opt-in to this recipe only. The streaming-vs-online reasoning in the gate comment is accurate: forward raises on a missing base_model_outputs rather than training on nothing.
Risk
Low. The three previously-blocking defects are genuinely fixed, and the fixes are the right shape rather than symptom patches. Both new findings are confined to two lines, are non-crashing in the common case, and have small local fixes. The resume path remains the least-exercised part of this change — the config-inertness issue is the kind a save → from_pretrained → setup_external_draft round-trip test would have caught, which is the test @kevalmorabia97 already asked for alongside finding 1.
Also still open from the collaborator review, outside the two files I was asked to focus on but worth not losing: cp_size > 1 is accepted for this recipe and model.eagle_cp_size is still set on the external draft at main.py:454, and the base tokenizer is loaded twice on the fresh path (:360 and :234).
🤖 Generated with Claude Code
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@kevalmorabia97 any tips for linux timeout on these ci tests? |
|
/claude review focus on examples/speculative_decoding/main.py and |
| external_cfg: dict = recipe.external.model_dump() | ||
| # The draft's own tokenizer; the base's is used only for its lm_head. | ||
| tokenizer = transformers.AutoTokenizer.from_pretrained( | ||
| recipe.draft_model_name_or_path, | ||
| model_max_length=training_args.training_seq_len, | ||
| trust_remote_code=recipe.model.trust_remote_code, | ||
| ) | ||
| if not ModeloptStateManager.has_state_for_mode_type("speculative", model=draft): | ||
| mtsp.convert(draft, [("external", external_cfg)]) |
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[IMPORTANT Compatibility] The recipe's external: section is silently inert on the resume path — re-raising, this is the one finding from my last pass that is still open (the mode-agnostic is_converted issue is fixed by the switch to has_state_for_mode_type("speculative", ...), which is the right call).
Skipping mtsp.convert also skips modify(). The draft's objective was already set by restore_external_draft_model → convert_to_external_draft_model(model, config) → modify(config) (external/conversion.py:65), and that config is the one reconstructed from the checkpoint's modelopt_state.pth (opt/conversion.py:632-638), not the recipe's. So external_cfg at line 221 is computed and dropped on the floor whenever this branch is taken.
Why it matters: resuming with an edited external_loss / external_top_k / external_top_p trains against the old objective, while pprint(recipe) at :280 and the recipe written next to the checkpoint both report the new one. Nothing fails, nothing warns — the loss curve stays plausible. That is the same silent-corruption class as the four defects the PR description says end-to-end validation surfaced, and it is exactly what a save → from_pretrained → setup_external_draft round-trip test (already requested alongside the double-convert fix) would expose.
Either honour the recipe by re-applying modify() on the restored draft, or refuse the mismatch. The one outcome to avoid is honouring neither:
if not ModeloptStateManager.has_state_for_mode_type("speculative", model=draft):
mtsp.convert(draft, [("external", external_cfg)])
else:
# Restored drafts carry the config saved in the checkpoint, not the recipe's, so
# re-apply modify() -- otherwise an edited external_loss / top_k / top_p is
# silently ignored while the printed recipe reports the new value.
draft.modify(ExternalDraftConfig(**external_cfg))If re-applying is deliberately not wanted (e.g. you consider the saved objective authoritative), then raise on a difference instead, and say so in the docstring — the current docstring says only that "the validations and the head attachment below still run", which reads as though the config were applied.
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Claude review — focused re-check of the three prior findings (round 2)
Scope applied. Per the trigger comment I reviewed only examples/speculative_decoding/main.py and modelopt/torch/speculative/plugins/modeling_fakebase.py, and skipped tests/, README.md and CHANGELOG.rst. I re-read the context those two files depend on to verify the fixes end to end (opt/conversion.py, opt/mode.py, opt/dynamic.py, opt/plugins/huggingface.py, external/conversion.py, hf_external.py, hf_eagle.py, hf_dflash.py, hf_training_args.py, load_vlm_or_llm, the shipped external_draft.yaml).
Findings: CRITICAL 0 · IMPORTANT 1 · SUGGESTION 0
The three findings asked about are resolved
1. Double-convert on the HF resume path — fixed. main.py:228 guards mtsp.convert with ModeloptStateManager.has_state_for_mode_type("speculative", model=draft), and the two validations plus attach_base_lm_head still run unconditionally below it, which is what the resume path needs. The mechanism checks out: _patch_model_init_for_modelopt calls restore_from_modelopt_state inside the patched __init__ (opt/plugins/huggingface.py:90-93) and _new_save_pretrained writes modelopt_state.pth whenever the model is converted (:110-112), so a checkpoint from this mode does come back already converted and the second convert would have hit the MRO error from register_subclass.
This also closes the second of the two issues I raised last round: has_state_for_mode_type consults the "speculative" registry (opt/conversion.py:199-200), so a draft carrying only quantization state no longer skips conversion and dies on the next line with a bare AttributeError. I checked the probe itself for side effects — on a fresh unconverted draft modelopt_state() builds a manager over a throwaway module and returns an empty state rather than raising, and external's update_for_save is the inherited no-op, so nothing is mutated.
2. The dry_run gate — fixed. main.py:207 is now if not dry_run and recipe.data.mode == "online". This matters for the shipped recipe specifically: external_draft.yaml leaves every data-source field unset and DataArguments._check_mode_requirements recomputes mode from those fields (hf_training_args.py:90-96), so the file as shipped always resolves to "online" — before the fix --dry_run could not complete even once against it. It now has the same shape as the data.data_path check at :269, and the docstring says so.
3. named_modules aliasing — fixed. find_final_norm_path is a get_submodule loop over _FINAL_NORM_PATHS catching AttributeError per path. get_submodule walks the attribute path and is indifferent to aliasing, so a norm instance also registered under an earlier path is still found; the named_modules() de-duplication hole is closed. get_submodule also raises AttributeError (rather than returning something odd) when an intermediate name exists but is not an nn.Module, so the prefixes in _FINAL_NORM_PATHS degrade correctly. The docstring records the why, which is worth the lines here since the obvious refactor back to a dict would silently reintroduce the bug. The helper is now the single implementation — hf_eagle.py:140 and hf_dflash.py:252 both call it and the example no longer reaches for a private name; the only remaining direct _FINAL_NORM_PATHS use is the unrelated weight-key lookup at modeling_fakebase.py:346. Narrowing except Exception to except AttributeError is a real improvement over what it replaced, and safe for the DynamicModule callers, whose __getattr__ bottoms out in nn.Module.__getattr__.
One finding still open
The recipe external: section is silently inert on resume (main.py:221-229) — a re-raise, not a new issue. Skipping convert also skips modify(), and the restored draft was configured from the config saved in modelopt_state.pth, not the recipe's (opt/conversion.py:632-638 -> external/conversion.py:65). So external_cfg is computed at :221 and discarded, and resuming with an edited external_loss / external_top_k / external_top_p trains against the old objective while pprint(recipe) at :280 reports the new one. Nothing fails and nothing warns — the loss curve stays plausible, which is the same silent-corruption class as the four defects the description says end-to-end validation surfaced. Re-apply modify() on the restored draft, or refuse the mismatch; the outcome to avoid is honouring neither. A save -> from_pretrained -> setup_external_draft round-trip test — the one @kevalmorabia97 already asked for alongside finding 1 — would cover both this and the double-convert fix in the same test.
Also checked, and sound
The resume-path load_vlm_or_llm call passes use_offline_training=True alongside use_fake_base, so it reaches the FakeBaseModel branch at utils.py:668 and the comment at :322-325 now describes what the code does. base_model.config.vocab_size is safe on every route this reaches: with use_offline_training=True a VLM base goes to FakeBaseModel at utils.py:722 regardless of use_fake_base, so the vocab_size=None parent config that would have broken validate_against_base is no longer reachable. device=training_args.device remains the right choice over local_rank arithmetic for the CUDA_VISIBLE_DEVICES-pinned case, and attach_base_lm_head deep-copies before freezing so requires_grad=False never reaches the base's own (possibly tied) head. Overwriting the checkpoint tokenizer at :335 with the draft-path one is correct rather than wasteful — the :309 load omits model_max_length, which setup_external_draft supplies. aux_hidden_states_optional has a real runtime path through eagle_utils.py:176 to OfflineSupervisedDataset and stays opt-in to this recipe only.
Risk
Low. All three previously-blocking defects are genuinely fixed, and the fixes are the right shape rather than symptom patches. The single remaining finding is confined to two lines, never crashes, and has a small local fix — but it is a silently-wrong failure on resume, so it should land before merge rather than after.
Outside the two files I was asked to focus on, and still unanswered from the collaborator review: cp_size > 1 is accepted for this recipe and model.eagle_cp_size is set on the external draft at main.py:454, and the base tokenizer is loaded twice on the fresh path (:360 and :234).
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/claude review focus on examples/speculative_decoding/main.py and |
| elif isinstance(recipe, ModelOptExternalDraftRecipe): | ||
| # This mode inverts the usual relationship: we train a pretrained draft, and | ||
| # `model` loaded above is the base, kept only for its lm_head and vocabulary. | ||
| model, tokenizer = setup_external_draft( |
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[IMPORTANT Performance] The dry_run gate unblocked --dry_run on the shipped recipe, but the fresh path still derives use_offline_training from the same mode == "online" it now tolerates — so the one configuration the gate was added to support is also the one that skips the cheap base.
use_offline_training = recipe.data.mode != "online" (:300) and load_vlm_or_llm(..., use_offline_training=use_offline_training) (:361). external_draft.yaml ships every data-source field unset and DataArguments._check_mode_requirements recomputes mode from those fields (hf_training_args.py:96), so the file as shipped resolves to "online". After the gate at :207 lets dry_run through, use_offline_training is False, load_vlm_or_llm never reaches the use_offline_training and use_fake_base branch (utils.py:668), and use_fake_base_for_offline: true is silently ignored — every base decoder layer materializes on every rank just to borrow an lm_head and a final norm. That is exactly the inversion the resume-path fix removed at :336; the fresh path did not get the same treatment.
A second consequence on the same line: with use_offline_training=False a VLM base returns the real multimodal model, and base_vocab_size = base_model.config.vocab_size at :221 then reads the parent config rather than the nested text config. That parent either has no vocab_size or carries None, so validate_against_base's int(base_vocab_size) dies with a bare TypeError/AttributeError instead of performing the vocabulary check it exists for. With use_offline_training=True the VLM route goes to FakeBaseModel at utils.py:722 regardless of use_fake_base, which is why the resume path is already safe here.
Both follow from one line. Mirror what :336 already does — this mode has no online path, so True is unconditionally correct:
elif isinstance(recipe, ModelOptExternalDraftRecipe):with the load above becoming, for this recipe, use_offline_training=True. The smallest version is to hoist it next to the derivation:
use_offline_training = recipe.data.mode != "online"
# External-draft training has no online path (see setup_external_draft), and the
# shipped recipe carries no data-source field, so mode resolves to "online" under
# --dry_run. Without this, load_vlm_or_llm ignores use_fake_base_for_offline and
# materialises every base decoder layer.
if isinstance(recipe, ModelOptExternalDraftRecipe):
use_offline_training = Truewhich also lets :336 drop its unconditional True and go back to passing the variable.
| # already run with the values this draft was last trained under. Re-apply the | ||
| # recipe's: otherwise an edited external_loss / top_k / top_p is dropped while | ||
| # the banner and the recipe saved beside the checkpoint report the new one. | ||
| draft.modify(ExternalDraftConfig(**external_cfg)) |
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[SUGGESTION] The re-apply fixes the runtime objective but leaves the stored config stale, so the state no longer describes the model.
modify() writes the temp attributes (external_model.py:40-43); nothing writes back to _modelopt_state. external's update_for_save is the inherited no-op (opt/mode.py:215), so update_last_state_before_save doesn't sync it either (opt/conversion.py:306-312), and _new_save_pretrained serializes modelopt_state(self) verbatim (opt/plugins/huggingface.py:110-112). Net effect: resume with an edited external_loss trains the new objective (which is the point of this fix) but the next checkpoint records the old one, and keeps recording it for every subsequent save.
Low impact because these four fields are training-only — the draft exports as a plain HF checkpoint and nothing in the serving path reads them — and because a main.py resume always re-applies from the recipe. It does mean a bare mto.restore on that checkpoint hands back a draft configured with the objective it was never trained under, which is the restore-fidelity property the rest of this mode is careful about. Worth a line if the fix is cheap; fine to leave with a comment saying why the divergence is tolerable.
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Claude review — focused re-check of the three prior findings (round 3)
Scope applied. Per the trigger comment I reviewed only examples/speculative_decoding/main.py and modelopt/torch/speculative/plugins/modeling_fakebase.py, and skipped tests/ and docs (README.md, CHANGELOG.rst). I re-read the context those two files depend on to check the fixes end to end (opt/conversion.py, opt/mode.py, opt/dynamic.py, opt/plugins/huggingface.py, external/conversion.py, external/external_model.py, hf_external.py, hf_eagle.py, hf_dflash.py, hf_training_args.py, load_vlm_or_llm, the shipped external_draft.yaml).
Findings: CRITICAL 0 · IMPORTANT 1 · SUGGESTION 1
All three findings asked about are resolved
1. Double-convert on the HF resume path — fixed. main.py:229 guards mtsp.convert with ModeloptStateManager.has_state_for_mode_type("speculative", model=draft), and the two validations plus attach_base_lm_head still run unconditionally below it, which is what the resume path needs. The mechanism holds: _patch_model_init_for_modelopt calls restore_from_modelopt_state inside the patched __init__ (opt/plugins/huggingface.py:90-93) and _new_save_pretrained writes modelopt_state.pth whenever the model is converted (:110-112), so a checkpoint from this mode does come back already converted and a second convert would have hit the MRO error from register_subclass. The "speculative"-scoped probe (opt/conversion.py:199-200) is the right narrowing over is_converted — a draft carrying only quantization state no longer skips conversion and dies on the next line with a bare AttributeError.
2. The dry_run gate — fixed. main.py:207 is now if not dry_run and recipe.data.mode == "online", matching the shape of the data.data_path check at :276, with the docstring saying so.
3. named_modules aliasing — fixed. find_final_norm_path is a get_submodule loop over _FINAL_NORM_PATHS catching AttributeError per path. get_submodule walks the attribute path and is indifferent to aliasing, so a norm instance also registered under an earlier path is still found; the named_modules() de-duplication hole is closed. It also raises AttributeError when an intermediate name exists but is not an nn.Module, so the prefixes in the list degrade correctly, and it is safe for the DynamicModule callers whose __getattr__ bottoms out in nn.Module.__getattr__ (opt/dynamic.py:786). The helper is now the single implementation — hf_eagle.py:140 and hf_dflash.py:252 both call it and the example no longer reaches for a private name. Narrowing except Exception to except AttributeError is a real improvement over what it replaced, and the docstring records the why, which earns its lines since the obvious refactor back to a dict would silently reintroduce the bug.
The round-2 finding is also resolved. main.py:231-236 now re-applies draft.modify(ExternalDraftConfig(**external_cfg)) when conversion is skipped, so an edited external_loss / external_top_k / external_top_p reaches the running model instead of being computed and discarded. The config fields read straight off the module at hf_external.py:292-310, so the re-apply does change the objective. One residual noted as a SUGGESTION below.
One new finding
The dry_run gate left the fresh path's use_offline_training behind (main.py:394) — IMPORTANT, and the one issue I'd want fixed before merge.
use_offline_training = recipe.data.mode != "online" (:300) still feeds the fresh-path load at :361. Because external_draft.yaml ships every data-source field unset and DataArguments recomputes mode from those fields (hf_training_args.py:96), the file as shipped resolves to "online" — so the very configuration finding 2 was fixed to admit is also the one where use_offline_training is False, load_vlm_or_llm never reaches its use_offline_training and use_fake_base branch (utils.py:668), and use_fake_base_for_offline: true is silently ignored. Every base decoder layer materializes on every rank to borrow an lm_head and a final norm. That is the same inversion the resume-path fix removed at :336; the fresh path did not get the same treatment. Same line also makes a VLM base return the real multimodal model, so base_model.config.vocab_size at :221 reads the parent config and validate_against_base dies on int(None) instead of performing the vocabulary check — the failure the resume path is already immune to, since use_offline_training=True routes VLMs to FakeBaseModel at utils.py:722 regardless of use_fake_base. Both follow from one line; the inline comment has the diff.
The SUGGESTION (main.py:236) is that the re-apply fixes the runtime objective but not the stored one: modify() writes temp attributes, external's update_for_save is the inherited no-op, so the next checkpoint still records the pre-edit objective. Low impact — these four fields are training-only and the exported draft is a plain HF checkpoint — but a bare mto.restore hands back a draft configured with an objective it was never trained under.
Also checked, and sound
The resume-path load_vlm_or_llm call passes use_offline_training=True alongside use_fake_base, so it does reach the FakeBaseModel branch and the comment at :329-332 describes what the code does. device=training_args.device remains the right choice over local_rank arithmetic for the CUDA_VISIBLE_DEVICES-pinned case, and attach_base_lm_head deep-copies before freezing so requires_grad=False never reaches the base's own (possibly tied) head. Overwriting the checkpoint tokenizer at :342 with the draft-path one is correct rather than wasteful — the :316 load omits model_max_length, which setup_external_draft supplies. aux_hidden_states_optional has a real runtime path to OfflineSupervisedDataset and stays opt-in to this recipe only. I checked has_state_for_mode_type for side effects on a fresh unconverted draft: modelopt_state() builds a manager over a throwaway module and returns an empty state rather than raising, and the inherited update_for_save mutates nothing.
Risk
Low. All three previously-blocking defects are genuinely fixed, and the round-2 config-inertness issue with them. The fixes are the right shape rather than symptom patches. The one new finding is a single line, is a memory regression plus a confusing error rather than silent corruption, and sits on the path --dry_run against the shipped recipe takes — which is the plumbing test the flag exists for, so it is likely to be hit.
Outside the two files I was asked to focus on, and still unanswered from the collaborator review: cp_size > 1 is accepted for this recipe and model.eagle_cp_size is set on the external draft at main.py:462, and the base tokenizer is loaded twice on the fresh path (:367 and :241).
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/claude review focus on examples/speculative_decoding/main.py and |
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/claude review focus on examples/speculative_decoding/main.py and |
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@kevalmorabia97 both leftovers from your re-review are fixed:
Also fixed the follow-on the bot found: the fresh path still derived Ready for another look when you have time. |
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Re-reviewed at 5b71c30. All the earlier findings are now resolved:
- Resume double-convert: fixed. The conversion is skipped when the draft is already converted, and a save →
from_pretrained→setup_external_drafttest covers it. That test also checks thatarchitecturesstaysLlamaForCausalLM. --dry_run: fixed. The online gate is skipped underdry_run, anduse_offline_trainingis pinned to True for this recipe, so a dry run no longer loads the full base.- Norm aliasing: fixed.
find_final_norm_pathnow resolves each path withget_submodule. cp_size > 1: now rejected, with a test.- Base tokenizer: the fresh path now passes it in, so it is loaded once.
I ran tests/unit/examples/test_speculative_decoding_main.py, tests/unit/recipe, test_fakebase.py and the external-draft tests locally: all passed. On CI, the six new tests pass on Linux and Windows. The Windows job fails on a timeout in tests/unit/torch/export/test_fsdp2_parallel_export.py, which this PR does not touch.
One new finding, inline: the resume path re-applies the recipe's external config to the live model but not to the stored modelopt state, so the saved checkpoint keeps the old objective. There is a one-line fix in the suggestion. Also one nit on the mode check.
Registers the speculative_external recipe type, its schema class, and the external_draft.yaml recipe, and teaches main.py to train the mode added in [2/8]. The recipe inverts the usual relationship: model.model_name_or_path is the base, used only for its lm_head and vocabulary, while draft_model_name_or_path names the pretrained draft that is trained. The vocabulary check runs before training because a mismatch otherwise yields a plausible loss curve and a draft with no acceptance. Documents the mode in the example README and adds the changelog entry for the series. Signed-off-by: Peter Hicks <phicks@nvidia.com> Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Peter Hicks <phicks@nvidia.com>
Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com> Signed-off-by: Peter Hicks <60696413+phicksy@users.noreply.github.com>
The HF-resume path skips mtsp.convert when the checkpoint already carries speculative state, which also skips modify(). The restored draft therefore keeps the objective saved in the checkpoint while the printed recipe reports the edited one, so resuming with a changed external_loss trains the old objective silently. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Peter Hicks <phicks@nvidia.com>
… base tokenizer The fresh path still derived use_offline_training from data.mode. The shipped recipe carries no data-source field, so under --dry_run it resolves to "online", load_vlm_or_llm ignores use_fake_base_for_offline, and every base decoder layer materialises on every rank -- the same inversion already fixed on the resume path. Also reject cp_size > 1, which this mode has no implementation for but was silently accepting, and let the fresh path hand over the base tokenizer it has already loaded instead of fetching it twice. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Peter Hicks <phicks@nvidia.com>
modify() writes only the live attributes, so a resume with an edited external: section trained the new objective while the next checkpoint still recorded the old one -- and a restore outside main.py handed back that stale objective. Update the state manager's config alongside modify(). Also refuse a draft restored from a different speculative mode: the guard asked only whether some speculative mode was present, so an eagle or dflash checkpoint skipped the convert and failed later on an unrelated attribute. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Peter Hicks <phicks@nvidia.com>
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@kevalmorabia97 anything more I need to do on this guy? |
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Re-reviewed at e4fa8a2. Both points from my last review are addressed, and I have no further findings.
- Stored config: the manager's config is now updated alongside
modify(). The extended test saves and reloads after the resume and checks fortvd_deployin the reloaded draft. - Mode check: the conversion is skipped only for an
externalstate. Any other speculative mode is refused with a clear message, and there is a test for an Eagle checkpoint.
I ran tests/unit/examples/test_speculative_decoding_main.py, tests/unit/recipe, test_fakebase.py and the external-draft tests locally: 580 passed. CI on this head was still pending when I checked.
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LGTM. Thanks for working through all the rounds.
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/ok to test e4fa8a2 |
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What does this PR do?
Third PR of 8 to support external draft training for LPU systems with ModelOpt.
Type of change: new feature
Registers the
speculative_externalrecipe type, its schema class, andexternal_draft.yaml, and teachesmain.pyto train the mode from [2/8]. The recipeinverts the usual relationship:
model.model_name_or_pathis the base, used only for itslm_head and vocabulary, while
draft_model_name_or_pathnames the pretrained draft that istrained.
The base final norm is resolved through
_FINAL_NORM_PATHSrather than assumed atbase_model.model.norm;FakeBaseModelkeeps it at.norm, so the assumed path alwaysreturned
None.Documents the mode in the example README and carries the changelog entry for the series.
Testing
pytest tests/unit/recipe tests/unit/torch/speculative; recipe loads end to end andresolves
RecipeType.SPECULATIVE_EXTERNAL.End-to-end validation. The series was exercised by running the whole workflow on real
models, not unit tests alone: a teacher policy was dumped from a 27B target, three drafts were
trained against it concurrently from one dump -- two sharing the target's tokenizer, one needing
the cross-tokenizer vocabulary swap -- each for a full epoch, and each exported a plain HF
checkpoint whose
architecturesresolves to the draft's own class. Acceptance was then measuredfor all three under a matched target policy. That run surfaced four defects this series' unit
tests and several review passes had all missed, two of which corrupted training silently rather
than failing; all four are fixed here.
Before your PR is "Ready for review"
CONTRIBUTING.md: N/A — no copied code, no new dependenciesreported
CRITICAL 0 · IMPORTANT 1 · SUGGESTION 1and that IMPORTANT (the fresh-pathuse_offline_traininginversion) is fixed in92653da. Later triggers hit theworkflow's 30-minute cap without posting; see the thread.
Additional Information
OSS provenance. No line added by this PR is copied from another project, with or
without modification. Every new file carries only the NVIDIA Apache-2.0 header, nothing was
added to the
insert-licenseexclude list, and there are no new PIP dependencies. One itemdeclared for completeness:
_deployment_probs(in [2/8]) implements the standardtop-k-then-top-p filter, the same algorithm as
transformers.generation.logits_process, butwas written independently and is textually different. Flagged because the algorithm is
widely shared, not because the code is.
Part of an eight-PR series. Merge order: [1/8] -> [2/8] -> [3/8] -> [4/8] -> [5/8] -> [6/8] -> [7/8] -> [8/8].
Summary by CodeRabbit